Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Hendrik Blockeel,Kristian Kersting,Filip Železný Conference pro

Reverie

令人悲伤

Regret Bounds for Reinforcement Learning with Policy Advicevisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinforcement learning task at hand. We prove that RLPA has a sub-linear regret of . relative to the best input policy, and that both th

滔滔不绝的人

Exploiting Multi-step Sample Trajectories for Approximate Value Iterationunction approximators used in such methods typically introduce errors in value estimation which can harm the quality of the learned value functions. We present a new batch-mode, off-policy, approximate value iteration algorithm called Trajectory Fitted Q-Iteration (TFQI). This approach uses the sequ

LIKEN

animated

conjunctivitis

Iterative Model Refinement of Recommender MDPs Based on Expert Feedbacks review of the policy. We impose a constraint on the parameters of the model for every case where the expert’s recommendation differs from the recommendation of the policy. We demonstrate that consistency with an expert’s feedback leads to non-convex constraints on the model parameters. We refine t

肮脏

oblique

Continuous Upper Confidence Trees with Polynomial Exploration – Consistencyarch. However, the consistency is only proved in a the case where the action space is finite. We here propose a proof in the case of fully observable Markov Decision Processes with bounded horizon, possibly including infinitely many states, infinite action space and arbitrary stochastic transition k

护航舰

A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimizations field aim to find the optimizer of the function by requesting only a few function evaluations at carefully selected locations. An ideal algorithm should maintain a perfect balance between exploration (probing unexplored areas) and exploitation (focusing on promising areas) within the given evaluat

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